To increase variability of the training data, reduce overfitting and ensure rotational invariance of global descriptors, we use on-the-fly data augmentation.
For radar scan images, we use random erasing augmentation [1]} and random cyclical shift over \(x\) -axis (angular dimension).
For points clouds, it includes random jitter and random rotation around \(z\) -axis. We also adapted random erasing augmentation to remove 3D points within the randomly selected frontoparallel cuboid.
